原文:http://chenxiaoyu.org/blog/archives/226 官网地址:http://webpy.org/ web.py是一个小巧灵活的框架,最新稳定版是0.33。这里不介绍web开发部分,介绍下关于数据库的相关操作。 很多Pyer一开始都喜欢自己封装数据库操作类,本人亦如此。不过后来通过
原文:http://chenxiaoyu.org/blog/archives/226
官网地址:http://webpy.org/
web.py是一个小巧灵活的框架,最新稳定版是0.33。这里不介绍web开发部分,介绍下关于数据库的相关操作。
很多Pyer一开始都喜欢自己封装数据库操作类,本人亦如此。不过后来通过观摩web.py的源码,发现其数据库操作部分相当紧凑实用。推荐懒人可以尝试一下。
废话不多,先来安装,有两种方式:
1. easy_install方式,如果木有此工具,可以参考:http://chenxiaoyu.org/blog/archives/23
easy_install web.py
2. 下载源码编译。地址: http://webpy.org/static/web.py-0.33.tar.gz ,解压后执行:
python setup.py install
web.py安装算到此结束,如果想使用其中的db功能,还得借助与相应数据库操作模块,比如MySQLdb、psycopg2。如果需要尝试连接池(database pool)功能,还得装下DBUtils。这几个模块都可以通过easy_install来安装。
下面开始使用吧!
1. 导入模块,定义数据库连接db。
import web db = web.database(dbn='postgres', db='mydata', user='dbuser', pw='')
2. select 查询
# 查询表 entries = db.select('mytable') # where 条件 myvar = dict(name="Bob") results = db.select('mytable', myvar, where="name = $name") results = db.select('mytable', where="id>100") # 查询具体列 results = db.select('mytable', what="id,name") # order by results = db.select('mytable', order="post_date DESC") # group results = db.select('mytable', group="color") # limit results = db.select('mytable', limit=10) # offset results = db.select('mytable', offset=10)
3. 更新
db.update('mytable', where="id = 10", value1 = "foo")
4. 删除
db.delete('mytable', where="id=10")
5. 复杂查询
# count results = db.query("SELECT COUNT(*) AS total_users FROM users") print results[0].total_users # join results = db.query("SELECT * FROM entries JOIN users WHERE entries.author_id = users.id") # 防止SQL注入可以这么干 results = db.query("SELECT * FROM users WHERE id=$id", vars={'id':10})
6 多数据库操作 (web.py大于0.3)
db1 = web.database(dbn='mysql', db='dbname1', user='foo') db2 = web.database(dbn='mysql', db='dbname2', user='foo') print db1.select('foo', where='id=1') print db2.select('bar', where='id=5')
7. 事务
t = db.transaction() try: db.insert('person', name='foo') db.insert('person', name='bar') except: t.rollback() raise else: t.commit() # Python 2.5+ 可以用with from __future__ import with_statement with db.transaction(): db.insert('person', name='foo') db.insert('person', name='bar')

MySQL index cardinality has a significant impact on query performance: 1. High cardinality index can more effectively narrow the data range and improve query efficiency; 2. Low cardinality index may lead to full table scanning and reduce query performance; 3. In joint index, high cardinality sequences should be placed in front to optimize query.

The MySQL learning path includes basic knowledge, core concepts, usage examples, and optimization techniques. 1) Understand basic concepts such as tables, rows, columns, and SQL queries. 2) Learn the definition, working principles and advantages of MySQL. 3) Master basic CRUD operations and advanced usage, such as indexes and stored procedures. 4) Familiar with common error debugging and performance optimization suggestions, such as rational use of indexes and optimization queries. Through these steps, you will have a full grasp of the use and optimization of MySQL.

MySQL's real-world applications include basic database design and complex query optimization. 1) Basic usage: used to store and manage user data, such as inserting, querying, updating and deleting user information. 2) Advanced usage: Handle complex business logic, such as order and inventory management of e-commerce platforms. 3) Performance optimization: Improve performance by rationally using indexes, partition tables and query caches.

SQL commands in MySQL can be divided into categories such as DDL, DML, DQL, DCL, etc., and are used to create, modify, delete databases and tables, insert, update, delete data, and perform complex query operations. 1. Basic usage includes CREATETABLE creation table, INSERTINTO insert data, and SELECT query data. 2. Advanced usage involves JOIN for table joins, subqueries and GROUPBY for data aggregation. 3. Common errors such as syntax errors, data type mismatch and permission problems can be debugged through syntax checking, data type conversion and permission management. 4. Performance optimization suggestions include using indexes, avoiding full table scanning, optimizing JOIN operations and using transactions to ensure data consistency.

InnoDB achieves atomicity through undolog, consistency and isolation through locking mechanism and MVCC, and persistence through redolog. 1) Atomicity: Use undolog to record the original data to ensure that the transaction can be rolled back. 2) Consistency: Ensure the data consistency through row-level locking and MVCC. 3) Isolation: Supports multiple isolation levels, and REPEATABLEREAD is used by default. 4) Persistence: Use redolog to record modifications to ensure that data is saved for a long time.

MySQL's position in databases and programming is very important. It is an open source relational database management system that is widely used in various application scenarios. 1) MySQL provides efficient data storage, organization and retrieval functions, supporting Web, mobile and enterprise-level systems. 2) It uses a client-server architecture, supports multiple storage engines and index optimization. 3) Basic usages include creating tables and inserting data, and advanced usages involve multi-table JOINs and complex queries. 4) Frequently asked questions such as SQL syntax errors and performance issues can be debugged through the EXPLAIN command and slow query log. 5) Performance optimization methods include rational use of indexes, optimized query and use of caches. Best practices include using transactions and PreparedStatemen

MySQL is suitable for small and large enterprises. 1) Small businesses can use MySQL for basic data management, such as storing customer information. 2) Large enterprises can use MySQL to process massive data and complex business logic to optimize query performance and transaction processing.

InnoDB effectively prevents phantom reading through Next-KeyLocking mechanism. 1) Next-KeyLocking combines row lock and gap lock to lock records and their gaps to prevent new records from being inserted. 2) In practical applications, by optimizing query and adjusting isolation levels, lock competition can be reduced and concurrency performance can be improved.


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